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As financial institutions in the world drive towards achieving a cashless economy, by increasing citizens spending power and reducing the high cost of money handling, the use of credit cards is of great necessity for this purpose, hence, with this new drive for a cashless economy, there will be significant increase of the use of credit card and also fraudulent activities associated with it. This work serves as a proactive measure in detecting fraudulent activities regarding the credit card. The study presents a hierarchical temporal memory based model that can detect fraudulent transactions carried out with the use of credit card. A novel approach in machine learning known as the Cortical Learning Algorithm was adopted to build the credit card fraud detection model. The algorithm worked on the credit card data obtained from the UCI Repository, it converted the highly populated data to a sparse representation, and then used its learning columns to learn spatial patterns. The Object Oriented Analysis and Design methodology was used in this work and was implemented using Java programming language. The resulting model performed online learning and recorded high percentage accuracy.
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As financial institutions in the world drive towards achieving a cashless economy, by increasing citizens spending power and reducing the high cost of money handling, the use of credit cards is of great necessity for this purpose, hence, with this new drive for a cashless economy, there will be significant increase of the use of credit card and also fraudulent activities associated with it. This work serves as a proactive measure in detecting fraudulent activities regarding the credit card. The study presents a hierarchical temporal memory based model that can detect fraudulent transactions carried out with the use of credit card. A novel approach in machine learning known as the Cortical Learning Algorithm was adopted to build the credit card fraud detection model. The algorithm worked on the credit card data obtained from the UCI Repository, it converted the highly populated data to a sparse representation, and then used its learning columns to learn spatial patterns. The Object Oriented Analysis and Design methodology was used in this work and was implemented using Java programming language. The resulting model performed online learning and recorded high percentage accuracy.
Biografía del autor
Oghenekaro, Linda Uchenna is a Lecturer in the Department of Computer Science, University of Port Harcourt, Rivers State, Nigeria. She obtained her B.Sc (Hons) and M.Sc degrees in Computer Science from the University of Port Harcourt. Her research interest include Machine Learning, Distributed Database and Data Security.
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Credit Card Fraud Detection Using Cortical Learning Algorithm
Verlag:
LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659956783
ISBN 13: 9783659956782
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Oghenekaro LindaOghenekaro, Linda Uchenna is a Lecturer in the Department of Computer Science, University of Port Harcourt, Rivers State, Nigeria. She obtained her B.Sc (Hons) and M.Sc degrees in Computer Science from the University . Bestandsnummer des Verkäufers 159148383
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Credit Card Fraud Detection Using Cortical Learning Algorithm
Verlag:
LAP LAMBERT Academic Publishing Okt 2016, 2016
ISBN 10: 3659956783
ISBN 13: 9783659956782
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Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
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Taschenbuch. Zustand: Neu. Neuware -As financial institutions in the world drive towards achieving a cashless economy, by increasing citizens spending power and reducing the high cost of money handling, the use of credit cards is of great necessity for this purpose, hence, with this new drive for a cashless economy, there will be significant increase of the use of credit card and also fraudulent activities associated with it. This work serves as a proactive measure in detecting fraudulent activities regarding the credit card. The study presents a hierarchical temporal memory based model that can detect fraudulent transactions carried out with the use of credit card. A novel approach in machine learning known as the Cortical Learning Algorithm was adopted to build the credit card fraud detection model. The algorithm worked on the credit card data obtained from the UCI Repository, it converted the highly populated data to a sparse representation, and then used its learning columns to learn spatial patterns. The Object Oriented Analysis and Design methodology was used in this work and was implemented using Java programming language. The resulting model performed online learning and recorded high percentage accuracy.Books on Demand GmbH, Überseering 33, 22297 Hamburg 112 pp. Englisch. Bestandsnummer des Verkäufers 9783659956782
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Credit Card Fraud Detection Using Cortical Learning Algorithm
Verlag:
LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659956783
ISBN 13: 9783659956782
Neu
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Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - As financial institutions in the world drive towards achieving a cashless economy, by increasing citizens spending power and reducing the high cost of money handling, the use of credit cards is of great necessity for this purpose, hence, with this new drive for a cashless economy, there will be significant increase of the use of credit card and also fraudulent activities associated with it. This work serves as a proactive measure in detecting fraudulent activities regarding the credit card. The study presents a hierarchical temporal memory based model that can detect fraudulent transactions carried out with the use of credit card. A novel approach in machine learning known as the Cortical Learning Algorithm was adopted to build the credit card fraud detection model. The algorithm worked on the credit card data obtained from the UCI Repository, it converted the highly populated data to a sparse representation, and then used its learning columns to learn spatial patterns. The Object Oriented Analysis and Design methodology was used in this work and was implemented using Java programming language. The resulting model performed online learning and recorded high percentage accuracy. Bestandsnummer des Verkäufers 9783659956782
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Credit Card Fraud Detection Using Cortical Learning Algorithm
Verlag:
LAP LAMBERT Academic Publishing Okt 2016, 2016
ISBN 10: 3659956783
ISBN 13: 9783659956782
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Taschenbuch
Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -As financial institutions in the world drive towards achieving a cashless economy, by increasing citizens spending power and reducing the high cost of money handling, the use of credit cards is of great necessity for this purpose, hence, with this new drive for a cashless economy, there will be significant increase of the use of credit card and also fraudulent activities associated with it. This work serves as a proactive measure in detecting fraudulent activities regarding the credit card. The study presents a hierarchical temporal memory based model that can detect fraudulent transactions carried out with the use of credit card. A novel approach in machine learning known as the Cortical Learning Algorithm was adopted to build the credit card fraud detection model. The algorithm worked on the credit card data obtained from the UCI Repository, it converted the highly populated data to a sparse representation, and then used its learning columns to learn spatial patterns. The Object Oriented Analysis and Design methodology was used in this work and was implemented using Java programming language. The resulting model performed online learning and recorded high percentage accuracy. 112 pp. Englisch. Bestandsnummer des Verkäufers 9783659956782
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Credit Card Fraud Detection Using Cortical Learning Algorithm
Verlag:
LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659956783
ISBN 13: 9783659956782
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Paperback. Zustand: Brand New. 112 pages. 8.66x5.91x0.26 inches. In Stock. Bestandsnummer des Verkäufers 3659956783
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